csparse.jl-1's Introduction

CSparse.jl

A Julia implementation of some of the functions in the CSparse and CXSparse libraries developed by Tim Davis

The Julia functions stay very close to the C originals. Most of the differences are in changing 0-based indexing to 1-based indexing and in using the Julia CompositeKind rather than a pointer to a C struct. This also allows for checking consistency of dimensions.

For example, the C function cs_lsolve

#include "cs.h"
/* solve Lx=b where x and b are dense.  x=b on input, solution on output. */
csi cs_lsolve (const cs *L, double *x)
{
    csi p, j, n, *Lp, *Li ;
    double *Lx ;
    if (!CS_CSC (L) || !x) return (0) ;                     /* check inputs */
    n = L->n ; Lp = L->p ; Li = L->i ; Lx = L->x ;
    for (j = 0 ; j < n ; j++)
    {
        x [j] /= Lx [Lp [j]] ;
        for (p = Lp [j]+1 ; p < Lp [j+1] ; p++)
        {
            x [Li [p]] -= Lx [p] * x [j] ;
        }
    }
    return (1) ;
}

becomes

## solve Lx=b where x and b are dense.  x=b on input, solution on output.
function js_lsolve!{T}(L::SparseMatrixCSC{T}, x::StridedVector{T})
    m,n = size(L)
    if m != n error("Matrix L is $m by $n and should be square") end
    if length(x) != n error("Dimension mismatch") end
    Lp = L.colptr; Li = L.rowval; Lx = L.nzval
    for j in 1:n
        x[j] /= Lx[Lp[j]]
        for p in (Lp[j] + 1):(Lp[j+1] - 1)
            x[Li[p]] -= Lx[p] * x[j]
        end
    end
    x
end

js_lsolve{T}(L::SparseMatrixCSC{T}, x::StridedVector{T}) = js_lsolve!(L, copy(x))

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